A derivative-fidelity failure mode in physics-informed neural networks: strengthened benchmark evidence from function-value training
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arXiv:2609.13171v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) use automatic differentiation to impose differential-equation residuals, but good agreement in function values does not necessarily imply accurate derivatives. This paper formulates derivative fidelity as a fai…
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- 2026-09-15 04:00 · arXiv cs.LG
A derivative-fidelity failure mode in physics-informed neural networks: strengthened benchmark evidence from function-value training
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